Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval
[low, high) (includes low, but excludes high). In other words,
any value within the given interval is equally likely to be drawn
low (float or array_like of floats, optional) – Lower boundary of the output interval. All values generated will be
greater than or equal to low. The default value is 0.
high (float or array_like of floats) – Upper boundary of the output interval. All values generated will be
less than high. The default value is 1.0.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then
m * n * k samples are drawn. If size is None (default),
a single value is returned if low and high are both scalars.
Otherwise, mt.broadcast(low, high).size samples are drawn.
(m, n, k)
m * n * k
chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
gpu (bool, optional) – Allocate the tensor on GPU if True, False as default
dtype (data-type, optional) – Data-type of the returned tensor.
out – Drawn samples from the parameterized uniform distribution.
Tensor or scalar
Discrete uniform distribution, yielding integers.
Discrete uniform distribution over the closed interval [low, high].
Floats uniformly distributed over [0, 1).
Alias for random_sample.
Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1).
The probability density function of the uniform distribution is
anywhere within the interval [a, b), and zero elsewhere.
When high == low, values of low will be returned.
If high < low, the results are officially undefined
and may eventually raise an error, i.e. do not rely on this
function to behave when passed arguments satisfying that
Draw samples from the distribution:
>>> import mars.tensor as mt
>>> s = mt.random.uniform(-1,0,1000)
All values are within the given interval:
>>> mt.all(s >= -1).execute()
>>> mt.all(s < 0).execute()
Display the histogram of the samples, along with the
probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s.execute(), 15, normed=True)
>>> plt.plot(bins, mt.ones_like(bins).execute(), linewidth=2, color='r')